---
title: "COVID-Messaging Dashboard"
date: "2020-07"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    source_code: embed
    vertical_layout: fill
    theme: "spacelab"
---

```{r setup, include=FALSE}
library(plyr) 
library(leaflet)
library(leafpop)
library(purrr)
library(plotly)
library(sf)
library(cartogram)
library(scales)
library(sjmisc)
library(shiny)
library(albersusa)
library(highcharter)
library(dplyr)
library(viridisLite)
library(forecast)
library(treemap)
library(flexdashboard)
library(kableExtra)

library(rtweet)        # Used for extracting the tweets
library(tm)            # Text mining cleaning
library(stringr)       # Removing characters
library(qdapRegex)     # Removing URLs 
library(wordcloud2) 
library(wordcloud)
`%>%` <- dplyr::`%>%`

data("usgeojson")

# Load plotting data
load("~/Dropbox (Yale_FES)/YPCCC_19XW/Crockett/Prototype/R_Dashboard/keystate.Rda")
load("~/Dropbox (Yale_FES)/YPCCC_19XW/Crockett/Prototype/R_Dashboard/plot_data.rda")

tweet.counts <- read.csv('~/Dropbox (Yale_FES)/YPCCC_19XW/Crockett/Prototype/Raw_Data/tweets_counts.csv')
st.tweets <- tweet.counts[!duplicated(tweet.counts$state),]
names(st.tweets)[names(st.tweets)=='name'] <- 'name.governor'
st.tweets <- st.tweets %>% 
  filter(state %in% c("Alabama", "Alaska", "Arizona", "Arkansas", "California", "Colorado", 
"Connecticut", "Delaware", "Florida", "Georgia", "Hawaii", "Idaho", 
"Illinois", "Indiana", "Iowa", "Kansas", "Kentucky", "Louisiana", 
"Maine", "Maryland", "Massachusetts", "Michigan", "Minnesota", 
"Mississippi", "Missouri", "Montana", "Nebraska", "Nevada", "New Hampshire", 
"New Jersey", "New Mexico", "New York", "North Carolina", "North Dakota", 
"Ohio", "Oklahoma", "Oregon", "Pennsylvania", "Rhode Island", 
"South Carolina", "South Dakota", "Tennessee", "Texas", "Utah", 
"Vermont", "Virginia", "Washington", "West Virginia", "Wisconsin", 
"Wyoming")) %>% 
  select(state, party, name.governor, n)

```


Classifed Tweets Display
=======================================================================


Column {.tabset}
-----------------------------------------------------------------------

### Lockdown vs Economy

#### Deontological %

```{r}
# thm <- 
#   hc_theme(
#     chart = list(
#       backgroundColor = "transparent",
#       style = list(fontFamily = "Source Sans Pro")
#     ),
#     xAxis = list(
#       gridLineWidth = 1)
#     )

# Map tweets volumn
# highchart() %>%
#   hc_add_series_map(usgeojson, st.tweets, name = "State",
#                     value = "n", joinBy = c("woename", "state"),
#                     dataLabels = list(enabled = TRUE,
#                                       format = '{point.properties.postalcode}')) %>%
#   # hc_colorAxis(stops = colstops) %>%
#   hc_legend(valueDecimals = 0, valueSuffix = "%") %>%
#   hc_mapNavigation(enabled = TRUE) %>%
#   hc_add_theme(thm)


n <- 5
stops1 <- data.frame(q = 0:n/n,
                    c = brewer_pal(palette = "Greens")(6),
                    stringsAsFactors = FALSE)
util.stops <- list_parse2(stops1)

stops2 <- data.frame(q = 0:n/n,
                    c = brewer_pal(palette = "Purples")(6),
                    stringsAsFactors = FALSE)
deon.stops <- list_parse2(stops2)

s.plot <- s.state %>% 
  filter(variable=="lockdown vs. opening the economy")
  
highchart() %>%
  hc_add_series_map(usgeojson, s.plot, name = "Deontological %",
                    value = "deon.pct", joinBy = c("woename", "State_name"),
                    dataLabels = list(enabled = TRUE,
                                      format = '{point.properties.postalcode}')) %>%
  hc_colorAxis(minColor="#ffffff", maxColor="#6a51a3", stops = deon.stops, min=0, max=100) %>%
  hc_legend(valueDecimals = 0, valueSuffix = "%") %>%
  hc_mapNavigation(enabled = TRUE) %>%
  hc_add_theme(hc_theme_smpl())

```


#### Tweets/Statements 

```{r}
df.display <- df %>%
  select(date, source_person, State_name,  deon_text, format,  `lockdown vs. opening the economy`) %>%
  filter(!is.na(deon_text)) %>% 
  filter(`lockdown vs. opening the economy` == "D") %>% 
  select(-`lockdown vs. opening the economy`) %>% 
  rename('Date' = 'date', 'Person' = 'source_person',
         'State' = 'State_name', 'Source' = 'format',
         'Text' = 'deon_text')

 kable(df.display) %>%
  kable_styling() %>%
  scroll_box(width='550px')

```

### Medical Care

#### Deontological %

```{r}

s.plot <- s.state %>% 
  filter(variable=="rationing medical care")

highchart() %>%
  hc_add_series_map(usgeojson, s.plot, name = "Deontological %",
                    value = "deon.pct", joinBy = c("woename", "State_name"),
                    dataLabels = list(enabled = TRUE,
                                      format = '{point.properties.postalcode}')) %>%
  hc_colorAxis(minColor="#ffffff", maxColor="#6a51a3", stops = deon.stops, min=0, max=100) %>%
  hc_legend(valueDecimals = 0, valueSuffix = "%") %>%
  hc_mapNavigation(enabled = TRUE) %>%
  hc_add_theme(hc_theme_smpl())

```


#### Tweets/Statements

```{r}
df.display <- df %>%
  select(date, source_person, State_name,  deon_text, format,  `rationing medical care`) %>%
  filter(!is.na(deon_text)) %>% 
  filter(`rationing medical care` == "D") %>% 
  select(-`rationing medical care`) %>% 
  rename('Date' = 'date', 'Person' = 'source_person',
         'State' = 'State_name', 'Source' = 'format',
         'Text' = 'deon_text')

kable(df.display) %>%
  kable_styling() %>%
  scroll_box(width='550px')

```


### Contact Tracing

#### Deontological %

```{r}

s.plot <- s.state %>% 
  filter(variable=="contact tracing & surveillance")

highchart() %>%
  hc_add_series_map(usgeojson, s.plot, name = "Deontological %",
                    value = "deon.pct", joinBy = c("woename", "State_name"),
                    dataLabels = list(enabled = TRUE,
                                      format = '{point.properties.postalcode}')) %>%
  hc_colorAxis(minColor="#ffffff", maxColor="#6a51a3", stops = deon.stops, min=0, max=100) %>%
  hc_legend(valueDecimals = 0, valueSuffix = "%") %>%
  hc_mapNavigation(enabled = TRUE) %>%
  hc_add_theme(hc_theme_smpl())

```


#### Tweets/Statements

```{r}
df.display <- df %>%
  select(date, source_person, State_name,  deon_text, format,  `contact tracing & surveillance`) %>%
  filter(!is.na(deon_text)) %>% 
  filter(`contact tracing & surveillance` == "D") %>% 
  select(-`contact tracing & surveillance`) %>% 
  rename('Date' = 'date', 'Person' = 'source_person',
         'State' = 'State_name', 'Source' = 'format',
         'Text' = 'deon_text')

kable(df.display) %>%
  kable_styling() %>%
  scroll_box(width='550px')

```


### Vaccine Enforcement

#### Deontological %

```{r}

s.plot <- s.state %>% 
  filter(variable=="vaccine enforcement")

highchart() %>%
  hc_add_series_map(usgeojson, s.plot, name = "Deontological %",
                    value = "deon.pct", joinBy = c("woename", "State_name"),
                    dataLabels = list(enabled = TRUE,
                                      format = '{point.properties.postalcode}')) %>%
  hc_colorAxis(minColor="#ffffff", maxColor="#6a51a3", stops = deon.stops, min=0, max=100) %>%
  hc_legend(valueDecimals = 0, valueSuffix = "%") %>%
  hc_mapNavigation(enabled = TRUE) %>%
  hc_add_theme(hc_theme_smpl())

```


#### Tweets/Statements

```{r}
df.display <- df %>%
  select(date, source_person, State_name,  deon_text, format,  `vaccine enforcement`) %>%
  filter(!is.na(deon_text)) %>% 
  filter(`vaccine enforcement` == "D") %>% 
  select(-`vaccine enforcement`) %>% 
  rename('Date' = 'date', 'Person' = 'source_person',
         'State' = 'State_name', 'Source' = 'format',
         'Text' = 'deon_text')

kable(df.display) %>%
  kable_styling() %>%
  scroll_box(width='550px')

```


### PPE

#### Deontological %

```{r}

s.plot <- s.state %>% 
  filter(variable=="PPE/Healthcare allocation")

highchart() %>%
  hc_add_series_map(usgeojson, s.plot, name = "Deontological %",
                    value = "deon.pct", joinBy = c("woename", "State_name"),
                    dataLabels = list(enabled = TRUE,
                                      format = '{point.properties.postalcode}')) %>%
  hc_colorAxis(minColor="#ffffff", maxColor="#6a51a3", stops = deon.stops, min=0, max=100) %>%
  hc_legend(valueDecimals = 0, valueSuffix = "%") %>%
  hc_mapNavigation(enabled = TRUE) %>%
  hc_add_theme(hc_theme_smpl())

```


#### Tweets/Statements

```{r}
df.display <- df %>%
  select(date, source_person, State_name,  deon_text, format,  `PPE/Healthcare allocation`) %>%
  filter(!is.na(deon_text)) %>% 
  filter(`PPE/Healthcare allocation` == "D") %>% 
  select(-`PPE/Healthcare allocation`) %>% 
  rename('Date' = 'date', 'Person' = 'source_person',
         'State' = 'State_name', 'Source' = 'format',
         'Text' = 'deon_text')

kable(df.display) %>%
  kable_styling() %>%
  scroll_box(width='550px')

```

### Other

#### Deontological %

```{r}

s.plot <- s.state %>% 
  filter(variable=="Other")

highchart() %>%
  hc_add_series_map(usgeojson, s.plot, name = "Deontological %",
                    value = "deon.pct", joinBy = c("woename", "State_name"),
                    dataLabels = list(enabled = TRUE,
                                      format = '{point.properties.postalcode}')) %>%
  hc_colorAxis(minColor="#ffffff", maxColor="#6a51a3", stops = deon.stops, min=0, max=100) %>%
  hc_legend(valueDecimals = 0, valueSuffix = "%") %>%
  hc_mapNavigation(enabled = TRUE) %>%
  hc_add_theme(hc_theme_smpl())

```


#### Tweets/Statements

```{r}
df.display <- df %>%
  select(date, source_person, State_name,  deon_text, format,  `Other`) %>%
  filter(!is.na(deon_text)) %>% 
  filter(`Other` == "D") %>% 
  select(-`Other`) %>% 
  rename('Date' = 'date', 'Person' = 'source_person',
         'State' = 'State_name', 'Source' = 'format',
         'Text' = 'deon_text')

kable(df.display) %>%
  kable_styling() %>%
  scroll_box(width='550px')

```



Column {.tabset}
-----------------------------------------------------------------------

### Lockdown vs Economy

#### Consequentialist %

```{r}

s.plot <- s.state %>% 
  filter(variable=="lockdown vs. opening the economy")

highchart() %>%
  hc_add_series_map(usgeojson, s.plot, name = "Consequentialist %",
                    value = "util.pct", joinBy = c("woename", "State_name"),
                    dataLabels = list(enabled = TRUE,
                                      format = '{point.properties.postalcode}')) %>%
  hc_colorAxis(minColor="#ffffff", maxColor="#238b45", stops = util.stops, min=0, max=100) %>%
  hc_legend(valueDecimals = 0, valueSuffix = "%") %>%
  hc_mapNavigation(enabled = TRUE) %>%
  hc_add_theme(hc_theme_smpl())

```


#### Tweets/Statements

```{r}
df.display <- df %>%
  select(date, source_person, State_name,  util_text, format, `lockdown vs. opening the economy`) %>%
  filter(!is.na(util_text)) %>% 
  filter(`lockdown vs. opening the economy` == "U") %>% 
  select(-`lockdown vs. opening the economy`) %>% 
  rename('Date' = 'date', 'Person' = 'source_person',
         'State' = 'State_name', 'Source' = 'format',
         'Text' = 'util_text')

kable(df.display) %>%
  kable_styling() %>%
  scroll_box(width='550px')

```

### Medical Care

#### Consequentialist %

```{r}

s.plot <- s.state %>% 
  filter(variable=="rationing medical care")

highchart() %>%
  hc_add_series_map(usgeojson, s.plot, name = "Consequentialist %",
                    value = "util.pct", joinBy = c("woename", "State_name"),
                    dataLabels = list(enabled = TRUE,
                                      format = '{point.properties.postalcode}')) %>%
  hc_colorAxis(minColor="#ffffff", maxColor="#238b45", stops = util.stops, min=0, max=100) %>%
  hc_legend(valueDecimals = 0, valueSuffix = "%") %>%
  hc_mapNavigation(enabled = TRUE) %>%
  hc_add_theme(hc_theme_smpl())

```


#### Tweets/Statements

```{r}
df.display <- df %>%
  select(date, source_person, State_name,  util_text, format,  `rationing medical care`) %>%
  filter(!is.na(util_text)) %>% 
  filter(`rationing medical care` == "U") %>% 
  select(-`rationing medical care`) %>% 
  rename('Date' = 'date', 'Person' = 'source_person',
         'State' = 'State_name', 'Source' = 'format',
         'Text' = 'util_text')

kable(df.display) %>%
  kable_styling() %>%
  scroll_box(width='550px')

```


### Contact Tracing

#### Consequentialist %

```{r}

s.plot <- s.state %>% 
  filter(variable=="contact tracing & surveillance")

highchart() %>%
  hc_add_series_map(usgeojson, s.plot, name = "Consequentialist %",
                    value = "util.pct", joinBy = c("woename", "State_name"),
                    dataLabels = list(enabled = TRUE,
                                      format = '{point.properties.postalcode}')) %>%
  hc_colorAxis(minColor="#ffffff", maxColor="#238b45", stops = util.stops, min=0, max=100) %>%
  hc_legend(valueDecimals = 0, valueSuffix = "%") %>%
  hc_mapNavigation(enabled = TRUE) %>%
  hc_add_theme(hc_theme_smpl())

```


#### Tweets/Statements

```{r}
df.display <- df %>%
  select(date, source_person, State_name,  util_text, format,  `contact tracing & surveillance`) %>%
  filter(!is.na(util_text)) %>% 
  filter(`contact tracing & surveillance` == "U") %>% 
  select(-`contact tracing & surveillance`) %>% 
  rename('Date' = 'date', 'Person' = 'source_person',
         'State' = 'State_name', 'Source' = 'format',
         'Text' = 'util_text')

kable(df.display) %>%
  kable_styling() %>%
  scroll_box(width='550px')

```


### Vaccine Enforcement

#### Consequentialist %

```{r}

s.plot <- s.state %>% 
  filter(variable=="vaccine enforcement")

highchart() %>%
  hc_add_series_map(usgeojson, s.plot, name = "Consequentialist %",
                    value = "util.pct", joinBy = c("woename", "State_name"),
                    dataLabels = list(enabled = TRUE,
                                      format = '{point.properties.postalcode}')) %>%
  hc_colorAxis(minColor="#ffffff", maxColor="#238b45", stops = util.stops, min=0, max=100) %>%
  hc_legend(valueDecimals = 0, valueSuffix = "%") %>%
  hc_mapNavigation(enabled = TRUE) %>%
  hc_add_theme(hc_theme_smpl())

```


#### Tweets/Statements

```{r}
df.display <- df %>%
  select(date, source_person, State_name,  util_text, format,  `vaccine enforcement`) %>%
  filter(!is.na(util_text)) %>% 
  filter(`vaccine enforcement` == "U") %>% 
  select(-`vaccine enforcement`) %>% 
  rename('Date' = 'date', 'Person' = 'source_person',
         'State' = 'State_name', 'Source' = 'format',
         'Text' = 'util_text')

kable(df.display) %>%
  kable_styling() %>%
  scroll_box(width='550px')

```


### PPE

#### Consequentialist %

```{r}

s.plot <- s.state %>% 
  filter(variable=="PPE/Healthcare allocation")

highchart() %>%
  hc_add_series_map(usgeojson, s.plot, name = "Consequentialist %",
                    value = "util.pct", joinBy = c("woename", "State_name"),
                    dataLabels = list(enabled = TRUE,
                                      format = '{point.properties.postalcode}')) %>%
  hc_colorAxis(minColor="#ffffff", maxColor="#238b45", stops = util.stops, min=0, max=100) %>%
  hc_legend(valueDecimals = 0, valueSuffix = "%") %>%
  hc_mapNavigation(enabled = TRUE) %>%
  hc_add_theme(hc_theme_smpl())

```


#### Tweets/Statements

```{r}
df.display <- df %>%
  select(date, source_person, State_name,  util_text, format,  `PPE/Healthcare allocation`) %>%
  filter(!is.na(util_text)) %>% 
  filter(`PPE/Healthcare allocation` == "U") %>% 
  select(-`PPE/Healthcare allocation`) %>% 
  rename('Date' = 'date', 'Person' = 'source_person',
         'State' = 'State_name', 'Source' = 'format',
         'Text' = 'util_text')

kable(df.display) %>%
  kable_styling() %>%
  scroll_box(width='550px')

```

### Other

#### Consequentialist %

```{r}

s.plot <- s.state %>% 
  filter(variable=="Other")

highchart() %>%
  hc_add_series_map(usgeojson, s.plot, name = "Consequentialist %",
                    value = "util.pct", joinBy = c("woename", "State_name"),
                    dataLabels = list(enabled = TRUE,
                                      format = '{point.properties.postalcode}')) %>%
  hc_colorAxis(minColor="#ffffff", maxColor="#238b45", stops = util.stops, min=0, max=100) %>%
  hc_legend(valueDecimals = 0, valueSuffix = "%") %>%
  hc_mapNavigation(enabled = TRUE) %>%
  hc_add_theme(hc_theme_smpl())

```


#### Tweets/Statements

```{r}
df.display <- df %>%
  select(date, source_person, State_name,  util_text, format,  `Other`) %>%
  filter(!is.na(util_text)) %>% 
  filter(`Other` == "U") %>% 
  select(-`Other`) %>% 
  rename('Date' = 'date', 'Person' = 'source_person',
         'State' = 'State_name', 'Source' = 'format',
         'Text' = 'util_text')

kable(df.display) %>%
  kable_styling("striped") %>%
  scroll_box(width='550px')

```





```{r}

# https://juliasilge.com/blog/tidy-text-classification/
  
```


Wordcloud
=======================================================================

Column {.tabset}
-----------------------------------------------------------------------

### Lockdown vs Economy

#### Consequentialist %

```{r results='asis'}
# devtools::install_github("gaospecial/wordcloud2")
library(wordcloud2)

df.text1 <- df %>% 
  select(date, source_person, State_name,  util_text, format, `lockdown vs. opening the economy`) %>%
  filter(!is.na(util_text)) %>% 
  filter(`lockdown vs. opening the economy` == "U") 

df.text <- df.text1$util_text
df.text <- str_c(df.text, collapse = "")

df.text <-
  df.text %>%
  str_remove("\\n") %>%                   # remove linebreaks
  rm_twitter_url() %>%                    # Remove URLS
  rm_url() %>%
  str_remove_all("#\\S+") %>%             # Remove any hashtags
  str_remove_all("@\\S+") %>%             # Remove any @ mentions
  removeWords(stopwords("english")) %>%   # Remove common words (a, the, it etc.)
  removeNumbers() %>%
  stripWhitespace() %>%
  removeWords(c("amp"))                   # Final cleanup of other small changes

textCorpus <-
  Corpus(VectorSource(df.text)) %>%
  TermDocumentMatrix() %>%
  as.matrix()

textCorpus <- sort(rowSums(textCorpus), decreasing=TRUE)
textCorpus <- data.frame(word = names(textCorpus), freq=textCorpus, row.names = NULL)

wordcloud2(data = textCorpus, minRotation = 0, maxRotation = 0, ellipticity = 0.6, shape = "circle", color = rep_len(brewer.pal(9, "Greens"), nrow(textCorpus)))

```


#### Deontological %

```{r}
df.text1 <- df %>% 
  select(date, source_person, State_name,  deon_text, format, `lockdown vs. opening the economy`) %>%
  filter(!is.na(deon_text)) %>% 
  filter(`lockdown vs. opening the economy` == "D") 

df.text <- df.text1$deon_text
df.text <- str_c(df.text, collapse = "")

df.text <-
  df.text %>%
  str_remove("\\n") %>%                   # remove linebreaks
  rm_twitter_url() %>%                    # Remove URLS
  rm_url() %>%
  str_remove_all("#\\S+") %>%             # Remove any hashtags
  str_remove_all("@\\S+") %>%             # Remove any @ mentions
  removeWords(stopwords("english")) %>%   # Remove common words (a, the, it etc.)
  removeNumbers() %>%
  stripWhitespace() %>%
  removeWords(c("amp"))                   # Final cleanup of other small changes

textCorpus <-
  Corpus(VectorSource(df.text)) %>%
  TermDocumentMatrix() %>%
  as.matrix()

textCorpus <- sort(rowSums(textCorpus), decreasing=TRUE)
textCorpus <- data.frame(word = names(textCorpus), freq=textCorpus, row.names = NULL)

wordcloud2(data = textCorpus, minRotation = 0, maxRotation = 0, ellipticity = 0.6, shape = "circle", color = rep_len(brewer.pal(9, "Purples"), nrow(textCorpus)))

```

### Medical Care

#### Consequentialist %

```{r}

s.plot <- s.state %>% 
  filter(variable=="rationing medical care")

highchart() %>%
  hc_add_series_map(usgeojson, s.plot, name = "Consequentialist %",
                    value = "util.pct", joinBy = c("woename", "State_name"),
                    dataLabels = list(enabled = TRUE,
                                      format = '{point.properties.postalcode}')) %>%
  hc_colorAxis(minColor="#ffffff", maxColor="#238b45", stops = util.stops, min=0, max=100) %>%
  hc_legend(valueDecimals = 0, valueSuffix = "%") %>%
  hc_mapNavigation(enabled = TRUE) %>%
  hc_add_theme(hc_theme_smpl())

```


#### Deontological %

```{r}
df.display <- df %>%
  select(date, source_person, State_name,  util_text, format,  `rationing medical care`) %>%
  filter(!is.na(util_text)) %>% 
  filter(`rationing medical care` == "U") %>% 
  select(-`rationing medical care`) %>% 
  rename('Date' = 'date', 'Person' = 'source_person',
         'State' = 'State_name', 'Source' = 'format',
         'Text' = 'util_text')

kable(df.display) %>%
  kable_styling() %>%
  scroll_box(width='550px')

```


### Contact Tracing

#### Consequentialist %

```{r}

s.plot <- s.state %>% 
  filter(variable=="contact tracing & surveillance")

highchart() %>%
  hc_add_series_map(usgeojson, s.plot, name = "Consequentialist %",
                    value = "util.pct", joinBy = c("woename", "State_name"),
                    dataLabels = list(enabled = TRUE,
                                      format = '{point.properties.postalcode}')) %>%
  hc_colorAxis(minColor="#ffffff", maxColor="#238b45", stops = util.stops, min=0, max=100) %>%
  hc_legend(valueDecimals = 0, valueSuffix = "%") %>%
  hc_mapNavigation(enabled = TRUE) %>%
  hc_add_theme(hc_theme_smpl())

```


#### Deontological %

```{r}
df.display <- df %>%
  select(date, source_person, State_name,  util_text, format,  `contact tracing & surveillance`) %>%
  filter(!is.na(util_text)) %>% 
  filter(`contact tracing & surveillance` == "U") %>% 
  select(-`contact tracing & surveillance`) %>% 
  rename('Date' = 'date', 'Person' = 'source_person',
         'State' = 'State_name', 'Source' = 'format',
         'Text' = 'util_text')

kable(df.display) %>%
  kable_styling() %>%
  scroll_box(width='550px')

```


### Vaccine Enforcement

#### Consequentialist %

```{r}

s.plot <- s.state %>% 
  filter(variable=="vaccine enforcement")

highchart() %>%
  hc_add_series_map(usgeojson, s.plot, name = "Consequentialist %",
                    value = "util.pct", joinBy = c("woename", "State_name"),
                    dataLabels = list(enabled = TRUE,
                                      format = '{point.properties.postalcode}')) %>%
  hc_colorAxis(minColor="#ffffff", maxColor="#238b45", stops = util.stops, min=0, max=100) %>%
  hc_legend(valueDecimals = 0, valueSuffix = "%") %>%
  hc_mapNavigation(enabled = TRUE) %>%
  hc_add_theme(hc_theme_smpl())

```


#### Deontological %

```{r}
df.display <- df %>%
  select(date, source_person, State_name,  util_text, format,  `vaccine enforcement`) %>%
  filter(!is.na(util_text)) %>% 
  filter(`vaccine enforcement` == "U") %>% 
  select(-`vaccine enforcement`) %>% 
  rename('Date' = 'date', 'Person' = 'source_person',
         'State' = 'State_name', 'Source' = 'format',
         'Text' = 'util_text')

kable(df.display) %>%
  kable_styling() %>%
  scroll_box(width='550px')

```


### PPE

#### Consequentialist 

```{r}

s.plot <- s.state %>% 
  filter(variable=="PPE/Healthcare allocation")

highchart() %>%
  hc_add_series_map(usgeojson, s.plot, name = "Consequentialist %",
                    value = "util.pct", joinBy = c("woename", "State_name"),
                    dataLabels = list(enabled = TRUE,
                                      format = '{point.properties.postalcode}')) %>%
  hc_colorAxis(minColor="#ffffff", maxColor="#238b45", stops = util.stops, min=0, max=100) %>%
  hc_legend(valueDecimals = 0, valueSuffix = "%") %>%
  hc_mapNavigation(enabled = TRUE) %>%
  hc_add_theme(hc_theme_smpl())

```


#### Deontological

```{r}
df.display <- df %>%
  select(date, source_person, State_name,  util_text, format,  `PPE/Healthcare allocation`) %>%
  filter(!is.na(util_text)) %>% 
  filter(`PPE/Healthcare allocation` == "U") %>% 
  select(-`PPE/Healthcare allocation`) %>% 
  rename('Date' = 'date', 'Person' = 'source_person',
         'State' = 'State_name', 'Source' = 'format',
         'Text' = 'util_text')

kable(df.display) %>%
  kable_styling() %>%
  scroll_box(width='550px')

```

### Other

#### Consequentialist %

```{r}

s.plot <- s.state %>% 
  filter(variable=="Other")

highchart() %>%
  hc_add_series_map(usgeojson, s.plot, name = "Consequentialist %",
                    value = "util.pct", joinBy = c("woename", "State_name"),
                    dataLabels = list(enabled = TRUE,
                                      format = '{point.properties.postalcode}')) %>%
  hc_colorAxis(minColor="#ffffff", maxColor="#238b45", stops = util.stops, min=0, max=100) %>%
  hc_legend(valueDecimals = 0, valueSuffix = "%") %>%
  hc_mapNavigation(enabled = TRUE) %>%
  hc_add_theme(hc_theme_smpl())

```


#### Deontological %

```{r}
df.display <- df %>%
  select(date, source_person, State_name,  util_text, format,  `Other`) %>%
  filter(!is.na(util_text)) %>% 
  filter(`Other` == "U") %>% 
  select(-`Other`) %>% 
  rename('Date' = 'date', 'Person' = 'source_person',
         'State' = 'State_name', 'Source' = 'format',
         'Text' = 'util_text')

kable(df.display) %>%
  kable_styling("striped") %>%
  scroll_box(width='550px')

```
























































































































Lollipop
=======================================================================

```{r, fig.width=12, fig.height=8}
library(ggplot2)
library(dplyr)
library(hrbrthemes)


ggplot(s.state) +
  geom_segment( aes(x=variable, xend=variable, y=deon.pct, yend=util.pct), color="grey") +
  geom_point( aes(x=variable, y=deon.pct), color="#d6604d", size=3 ) +
  geom_point( aes(x=variable, y=util.pct), color="#5aae61", size=3 ) +
  theme_ipsum() +
  facet_wrap(~State_name, nrow = 3)+
  coord_flip()+ 
  xlab("") +
  ylab("Red - deontological % | Green - consequentialist %") +
  theme(legend.position = "none", axis.text = element_text(size=12), strip.text = element_text(size=15))


```